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130 results

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  • Rhodium Doping of Strontium Titanate for Enhanced Visible Light Absorption
  • Piyush Haluai, Peter A. Crozier
  • Journal: Microscopy and Microanalysis / Volume 28 / Issue S1 / August 2022
  • Published online by Cambridge University Press: 22 July 2022, pp. 1880-1882
  • Print publication: August 2022
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  • How to Get Something Out of Nothing (Almost!): Extracting Information from Noisy Data
  • Peter A. Crozier
  • Journal: Microscopy and Microanalysis / Volume 28 / Issue S1 / August 2022
  • Published online by Cambridge University Press: 22 July 2022, pp. 2976-2977
  • Print publication: August 2022
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  • Probing Response and Functionality in Active Materials Systems with In Situ Electron Microscopy
  • Peter A. Crozier
  • Journal: Microscopy and Microanalysis / Volume 28 / Issue S1 / August 2022
  • Published online by Cambridge University Press: 22 July 2022, pp. 158-159
  • Print publication: August 2022
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  • Exploring Blob Detection to Determine Atomic Column Positions and Intensities in Time-Resolved TEM Images with Ultra-Low Signal-to-Noise
  • Ramon Manzorro, Yuchen Xu, Joshua L. Vincent, Roberto Rivera, David S. Matteson, Peter A. Crozier
  • Journal: Microscopy and Microanalysis / Volume 28 / Issue 6 / December 2022
  • Published online by Cambridge University Press: 28 March 2022, pp. 1917-1930
  • Print publication: December 2022
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    Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision.

  • Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise
  • Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y. Sheth, Eero P. Simoncelli, David S. Matteson, Carlos Fernandez-Granda, Peter A. Crozier
  • Journal: Microscopy and Microanalysis / Volume 27 / Issue 6 / December 2021
  • Published online by Cambridge University Press: 16 September 2021, pp. 1431-1447
  • Print publication: December 2021
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    A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

  • Study of sodium metal plasmon using electron energy loss spectroscopy
  • Shize Yang, Peter Crozier
  • Journal: Microscopy and Microanalysis / Volume 27 / Issue S1 / August 2021
  • Published online by Cambridge University Press: 30 July 2021, pp. 624-625
  • Print publication: August 2021
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Page 1 of 7